Overview

Dataset statistics

Number of variables14
Number of observations266
Missing cells844
Missing cells (%)22.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.7 KiB
Average record size in memory225.9 B

Variable types

Categorical2
Unsupported2
Numeric10

Alerts

Country Name has a high cardinality: 266 distinct values High cardinality
Country Code has a high cardinality: 266 distinct values High cardinality
2000 is highly correlated with 2011 and 8 other fieldsHigh correlation
2011 is highly correlated with 2000 and 8 other fieldsHigh correlation
2012 is highly correlated with 2000 and 8 other fieldsHigh correlation
2013 is highly correlated with 2000 and 8 other fieldsHigh correlation
2014 is highly correlated with 2000 and 8 other fieldsHigh correlation
2015 is highly correlated with 2000 and 8 other fieldsHigh correlation
2016 is highly correlated with 2000 and 8 other fieldsHigh correlation
2017 is highly correlated with 2000 and 8 other fieldsHigh correlation
2018 is highly correlated with 2000 and 8 other fieldsHigh correlation
2019 is highly correlated with 2000 and 8 other fieldsHigh correlation
2000 is highly correlated with 2011 and 8 other fieldsHigh correlation
2011 is highly correlated with 2000 and 8 other fieldsHigh correlation
2012 is highly correlated with 2000 and 8 other fieldsHigh correlation
2013 is highly correlated with 2000 and 8 other fieldsHigh correlation
2014 is highly correlated with 2000 and 8 other fieldsHigh correlation
2015 is highly correlated with 2000 and 8 other fieldsHigh correlation
2016 is highly correlated with 2000 and 8 other fieldsHigh correlation
2017 is highly correlated with 2000 and 8 other fieldsHigh correlation
2018 is highly correlated with 2000 and 8 other fieldsHigh correlation
2019 is highly correlated with 2000 and 8 other fieldsHigh correlation
2000 is highly correlated with 2011 and 8 other fieldsHigh correlation
2011 is highly correlated with 2000 and 8 other fieldsHigh correlation
2012 is highly correlated with 2000 and 8 other fieldsHigh correlation
2013 is highly correlated with 2000 and 8 other fieldsHigh correlation
2014 is highly correlated with 2000 and 8 other fieldsHigh correlation
2015 is highly correlated with 2000 and 8 other fieldsHigh correlation
2016 is highly correlated with 2000 and 8 other fieldsHigh correlation
2017 is highly correlated with 2000 and 8 other fieldsHigh correlation
2018 is highly correlated with 2000 and 8 other fieldsHigh correlation
2019 is highly correlated with 2000 and 8 other fieldsHigh correlation
2000 is highly correlated with 2011 and 8 other fieldsHigh correlation
2011 is highly correlated with 2000 and 8 other fieldsHigh correlation
2012 is highly correlated with 2000 and 8 other fieldsHigh correlation
2013 is highly correlated with 2000 and 8 other fieldsHigh correlation
2014 is highly correlated with 2000 and 8 other fieldsHigh correlation
2015 is highly correlated with 2000 and 8 other fieldsHigh correlation
2016 is highly correlated with 2000 and 8 other fieldsHigh correlation
2017 is highly correlated with 2000 and 8 other fieldsHigh correlation
2018 is highly correlated with 2000 and 8 other fieldsHigh correlation
2019 is highly correlated with 2000 and 8 other fieldsHigh correlation
1990 has 266 (100.0%) missing values Missing
2000 has 34 (12.8%) missing values Missing
2011 has 29 (10.9%) missing values Missing
2012 has 30 (11.3%) missing values Missing
2013 has 31 (11.7%) missing values Missing
2014 has 31 (11.7%) missing values Missing
2015 has 31 (11.7%) missing values Missing
2016 has 32 (12.0%) missing values Missing
2017 has 31 (11.7%) missing values Missing
2018 has 31 (11.7%) missing values Missing
2019 has 32 (12.0%) missing values Missing
2020 has 266 (100.0%) missing values Missing
Country Name is uniformly distributed Uniform
Country Code is uniformly distributed Uniform
Country Name has unique values Unique
Country Code has unique values Unique
1990 is an unsupported type, check if it needs cleaning or further analysis Unsupported
2020 is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-04-03 16:48:14.735379
Analysis finished2022-04-03 16:48:25.705874
Duration10.97 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Country Name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct266
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Afghanistan
 
1
St. Lucia
 
1
Serbia
 
1
Seychelles
 
1
Sierra Leone
 
1
Other values (261)
261 

Length

Max length52
Median length9
Mean length12.40225564
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAmerican Samoa
5th rowAndorra

Common Values

ValueCountFrequency (%)
Afghanistan1
 
0.4%
St. Lucia1
 
0.4%
Serbia1
 
0.4%
Seychelles1
 
0.4%
Sierra Leone1
 
0.4%
Singapore1
 
0.4%
Sint Maarten (Dutch part)1
 
0.4%
Slovak Republic1
 
0.4%
Slovenia1
 
0.4%
Solomon Islands1
 
0.4%
Other values (256)256
96.2%

Length

2022-04-03T11:48:25.795606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20
 
4.0%
and12
 
2.4%
income11
 
2.2%
ida10
 
2.0%
africa9
 
1.8%
islands9
 
1.8%
asia8
 
1.6%
ibrd8
 
1.6%
middle7
 
1.4%
rep7
 
1.4%
Other values (310)404
80.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Country Code
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct266
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
AFG
 
1
LCA
 
1
SRB
 
1
SYC
 
1
SLE
 
1
Other values (261)
261 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)100.0%

Sample

1st rowAFG
2nd rowALB
3rd rowDZA
4th rowASM
5th rowAND

Common Values

ValueCountFrequency (%)
AFG1
 
0.4%
LCA1
 
0.4%
SRB1
 
0.4%
SYC1
 
0.4%
SLE1
 
0.4%
SGP1
 
0.4%
SXM1
 
0.4%
SVK1
 
0.4%
SVN1
 
0.4%
SLB1
 
0.4%
Other values (256)256
96.2%

Length

2022-04-03T11:48:25.905340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
afg1
 
0.4%
bhr1
 
0.4%
cpv1
 
0.4%
bdi1
 
0.4%
dza1
 
0.4%
asm1
 
0.4%
and1
 
0.4%
ago1
 
0.4%
atg1
 
0.4%
arg1
 
0.4%
Other values (256)256
96.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1990
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

2000
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct229
Distinct (%)98.7%
Missing34
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean96.01615547
Minimum0.10373125
Maximum1104.989938
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:48:26.015042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.10373125
5-th percentile3.646912122
Q110.92611079
median33.66094315
Q3109.5765782
95-th percentile400.5525323
Maximum1104.989938
Range1104.886207
Interquartile range (IQR)98.65046741

Descriptive statistics

Standard deviation147.712246
Coefficient of variation (CV)1.538410336
Kurtosis11.14238007
Mean96.01615547
Median Absolute Deviation (MAD)25.38079343
Skewness2.833920446
Sum22275.74807
Variance21818.90761
MonotonicityNot monotonic
2022-04-03T11:48:26.142701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.166378452
 
0.8%
11.018325342
 
0.8%
12.066948532
 
0.8%
2.650913741
 
0.4%
241.38012821
 
0.4%
17.505936661
 
0.4%
244.52847911
 
0.4%
35.805159821
 
0.4%
22.057977741
 
0.4%
105.74111221
 
0.4%
Other values (219)219
82.3%
(Missing)34
 
12.8%
ValueCountFrequency (%)
0.103731251
0.4%
0.373263711
0.4%
0.816501431
0.4%
1.684668841
0.4%
1.936119071
0.4%
2.478484621
0.4%
2.650913741
0.4%
2.703614191
0.4%
3.180717191
0.4%
3.521947011
0.4%
ValueCountFrequency (%)
1104.9899381
0.4%
686.62151211
0.4%
651.97701591
0.4%
558.30444481
0.4%
543.76887831
0.4%
529.08465041
0.4%
446.45976241
0.4%
436.21576541
0.4%
419.55317331
0.4%
413.4465831
0.4%

2011
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct234
Distinct (%)98.7%
Missing29
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean207.918636
Minimum0.19994101
Maximum2357.311262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:48:26.270584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.19994101
5-th percentile6.977573066
Q130.60639816
median91.04663502
Q3229.3779884
95-th percentile787.2433105
Maximum2357.311262
Range2357.111321
Interquartile range (IQR)198.7715902

Descriptive statistics

Standard deviation292.3558358
Coefficient of variation (CV)1.406106934
Kurtosis13.25083208
Mean207.918636
Median Absolute Deviation (MAD)71.29767961
Skewness2.927237077
Sum49276.71674
Variance85471.93475
MonotonicityNot monotonic
2022-04-03T11:48:26.405222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.67849942
 
0.8%
30.619511092
 
0.8%
27.957108842
 
0.8%
37.252797321
 
0.4%
54.253852891
 
0.4%
256.77521041
 
0.4%
484.72638491
 
0.4%
60.939677841
 
0.4%
593.18636911
 
0.4%
261.96012331
 
0.4%
Other values (224)224
84.2%
(Missing)29
 
10.9%
ValueCountFrequency (%)
0.199941011
0.4%
1.998917891
0.4%
2.836107641
0.4%
3.592527161
0.4%
4.130262951
0.4%
4.903186241
0.4%
5.727798471
0.4%
6.37829011
0.4%
6.402542571
0.4%
6.408883771
0.4%
ValueCountFrequency (%)
2357.3112621
0.4%
1353.7311871
0.4%
1123.8339541
0.4%
995.6508951
0.4%
977.57719221
0.4%
973.42502591
0.4%
968.42657851
0.4%
924.39683081
0.4%
913.93441331
0.4%
874.93657691
0.4%

2012
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct233
Distinct (%)98.7%
Missing30
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean206.2466264
Minimum0.22193613
Maximum2306.446252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:48:26.535635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.22193613
5-th percentile6.90419314
Q130.23291803
median95.80206247
Q3236.2088653
95-th percentile750.4362683
Maximum2306.446252
Range2306.224316
Interquartile range (IQR)205.9759472

Descriptive statistics

Standard deviation285.5268313
Coefficient of variation (CV)1.38439516
Kurtosis13.49866939
Mean206.2466264
Median Absolute Deviation (MAD)75.71015137
Skewness2.952177941
Sum48674.20382
Variance81525.5714
MonotonicityNot monotonic
2022-04-03T11:48:26.667351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133.32220132
 
0.8%
30.236238612
 
0.8%
28.319472522
 
0.8%
38.315901451
 
0.4%
50.820385091
 
0.4%
234.80951781
 
0.4%
459.77991441
 
0.4%
58.915180061
 
0.4%
565.01585131
 
0.4%
246.06614361
 
0.4%
Other values (223)223
83.8%
(Missing)30
 
11.3%
ValueCountFrequency (%)
0.221936131
0.4%
1.916567061
0.4%
2.746719791
0.4%
3.400250971
0.4%
4.046187991
0.4%
4.883189431
0.4%
5.644962551
0.4%
5.983394361
0.4%
6.165928821
0.4%
6.58392771
0.4%
ValueCountFrequency (%)
2306.4462521
0.4%
1327.5850171
0.4%
1201.1126681
0.4%
1028.7181991
0.4%
1007.7342281
0.4%
927.37332961
0.4%
926.2762211
0.4%
911.34620961
0.4%
850.08583921
0.4%
818.00220071
0.4%

2013
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct232
Distinct (%)98.7%
Missing31
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean215.0359483
Minimum0.19685547
Maximum2334.058189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:48:26.795010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.19685547
5-th percentile8.0691968
Q133.1956039
median100.1832182
Q3251.3566254
95-th percentile769.7533099
Maximum2334.058189
Range2333.861334
Interquartile range (IQR)218.1610215

Descriptive statistics

Standard deviation293.032579
Coefficient of variation (CV)1.36271438
Kurtosis12.53786174
Mean215.0359483
Median Absolute Deviation (MAD)78.64716399
Skewness2.845022111
Sum50533.44785
Variance85868.09237
MonotonicityNot monotonic
2022-04-03T11:48:26.926716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121.63417412
 
0.8%
33.172975012
 
0.8%
34.915926872
 
0.8%
39.651274221
 
0.4%
3.27644941
 
0.4%
495.63713771
 
0.4%
67.354147761
 
0.4%
598.85422871
 
0.4%
46.023275221
 
0.4%
319.61566321
 
0.4%
Other values (222)222
83.5%
(Missing)31
 
11.7%
ValueCountFrequency (%)
0.196855471
0.4%
2.705536081
0.4%
3.261047651
0.4%
3.27644941
0.4%
3.915740451
0.4%
4.625656671
0.4%
5.377670611
0.4%
5.730158751
0.4%
6.03038191
0.4%
6.440935361
0.4%
ValueCountFrequency (%)
2334.0581891
0.4%
1348.688351
0.4%
1133.9692941
0.4%
1044.278241
0.4%
1018.7835681
0.4%
1003.6803991
0.4%
999.92257541
0.4%
970.21423341
0.4%
870.52296861
0.4%
861.27327771
0.4%

2014
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct232
Distinct (%)98.7%
Missing31
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean218.6522759
Minimum0.19199969
Maximum2493.954287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:48:27.053448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.19199969
5-th percentile8.247821821
Q134.05970114
median102.4882646
Q3245.8131959
95-th percentile816.7745823
Maximum2493.954287
Range2493.762287
Interquartile range (IQR)211.7534948

Descriptive statistics

Standard deviation302.3223117
Coefficient of variation (CV)1.382662543
Kurtosis14.214111
Mean218.6522759
Median Absolute Deviation (MAD)80.49098352
Skewness2.973554978
Sum51383.28484
Variance91398.78017
MonotonicityNot monotonic
2022-04-03T11:48:27.192645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111.72856822
 
0.8%
32.624703962
 
0.8%
35.250894592
 
0.8%
43.10952031
 
0.4%
3.22405771
 
0.4%
479.30767551
 
0.4%
67.021745461
 
0.4%
619.94911871
 
0.4%
31.134689071
 
0.4%
232.18498661
 
0.4%
Other values (222)222
83.5%
(Missing)31
 
11.7%
ValueCountFrequency (%)
0.191999691
0.4%
2.950569211
0.4%
3.22405771
0.4%
3.698836911
0.4%
4.449043211
0.4%
4.973885231
0.4%
5.689970641
0.4%
6.144254871
0.4%
6.271538721
0.4%
6.67250821
0.4%
ValueCountFrequency (%)
2493.9542871
0.4%
1311.080161
0.4%
1088.031491
0.4%
1066.6668051
0.4%
1035.6976711
0.4%
1030.0796061
0.4%
985.53243321
0.4%
980.51939481
0.4%
895.57499921
0.4%
878.92437061
0.4%

2015
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct232
Distinct (%)98.7%
Missing31
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean199.961458
Minimum0.18093384
Maximum2426.216041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:48:27.320809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.18093384
5-th percentile7.482590668
Q130.67533593
median94.9418795
Q3223.9205908
95-th percentile750.0779248
Maximum2426.216041
Range2426.035107
Interquartile range (IQR)193.2452549

Descriptive statistics

Standard deviation277.4584813
Coefficient of variation (CV)1.387559803
Kurtosis18.00255582
Mean199.961458
Median Absolute Deviation (MAD)72.98447379
Skewness3.270418334
Sum46990.94263
Variance76983.20887
MonotonicityNot monotonic
2022-04-03T11:48:27.458412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111.60049262
 
0.8%
30.434039782
 
0.8%
35.629067642
 
0.8%
46.172288691
 
0.4%
2.98216751
 
0.4%
546.96584561
 
0.4%
73.380215231
 
0.4%
523.7826591
 
0.4%
28.898931391
 
0.4%
204.51675841
 
0.4%
Other values (222)222
83.5%
(Missing)31
 
11.7%
ValueCountFrequency (%)
0.180933841
0.4%
1.149899431
0.4%
2.98216751
0.4%
3.918757911
0.4%
4.461655981
0.4%
4.91385671
0.4%
5.888199811
0.4%
5.998420011
0.4%
6.027623511
0.4%
6.088136291
0.4%
ValueCountFrequency (%)
2426.2160411
0.4%
1102.6215331
0.4%
1069.9782851
0.4%
1060.0424971
0.4%
911.32545721
0.4%
880.63943571
0.4%
834.49320711
0.4%
834.36120191
0.4%
829.97715381
0.4%
768.84246161
0.4%

2016
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct231
Distinct (%)98.7%
Missing32
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean204.2075878
Minimum0.18248357
Maximum2508.255091
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:48:27.588232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.18248357
5-th percentile7.571506677
Q128.91579067
median92.12405827
Q3227.6904942
95-th percentile773.1062842
Maximum2508.255091
Range2508.072607
Interquartile range (IQR)198.7747035

Descriptive statistics

Standard deviation285.571797
Coefficient of variation (CV)1.398438717
Kurtosis18.42380832
Mean204.2075878
Median Absolute Deviation (MAD)72.14320335
Skewness3.299255891
Sum47784.57554
Variance81551.25124
MonotonicityNot monotonic
2022-04-03T11:48:27.999103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121.0113482
 
0.8%
28.617559452
 
0.8%
36.011667272
 
0.8%
45.727948631
 
0.4%
3.008696331
 
0.4%
581.60265911
 
0.4%
75.921703371
 
0.4%
521.89636911
 
0.4%
26.909229451
 
0.4%
210.29376631
 
0.4%
Other values (221)221
83.1%
(Missing)32
 
12.0%
ValueCountFrequency (%)
0.182483571
0.4%
3.008696331
0.4%
3.294950371
0.4%
3.581277641
0.4%
4.311458631
0.4%
5.652801891
0.4%
5.852074391
0.4%
5.890286911
0.4%
5.933523071
0.4%
6.731832291
0.4%
ValueCountFrequency (%)
2508.2550911
0.4%
1137.0248911
0.4%
1094.7174541
0.4%
1069.8638261
0.4%
921.68951581
0.4%
906.33806771
0.4%
854.87804571
0.4%
823.24444371
0.4%
816.47073351
0.4%
815.59248361
0.4%

2017
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct232
Distinct (%)98.7%
Missing31
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean213.1630997
Minimum0.19613857
Maximum2536.642307
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:48:28.124768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.19613857
5-th percentile7.624521154
Q130.03324797
median98.98725935
Q3236.024328
95-th percentile799.687839
Maximum2536.642307
Range2536.446168
Interquartile range (IQR)205.99108

Descriptive statistics

Standard deviation296.5392693
Coefficient of variation (CV)1.391137912
Kurtosis16.421173
Mean213.1630997
Median Absolute Deviation (MAD)79.99220345
Skewness3.137291414
Sum50093.32842
Variance87935.53826
MonotonicityNot monotonic
2022-04-03T11:48:28.256920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115.39247872
 
0.8%
29.169517172
 
0.8%
31.480474842
 
0.8%
49.596604981
 
0.4%
3.028209531
 
0.4%
73.281858621
 
0.4%
561.6321661
 
0.4%
5.063314651
 
0.4%
31.24134391
 
0.4%
222.51327941
 
0.4%
Other values (222)222
83.5%
(Missing)31
 
11.7%
ValueCountFrequency (%)
0.196138571
0.4%
3.028209531
0.4%
3.198019721
0.4%
3.49817051
0.4%
4.873355521
0.4%
5.063314651
0.4%
5.407833371
0.4%
5.832398511
0.4%
5.981180031
0.4%
6.081169921
0.4%
ValueCountFrequency (%)
2536.6423071
0.4%
1151.9449231
0.4%
1134.2210931
0.4%
1110.6624231
0.4%
1019.3294661
0.4%
1004.9354251
0.4%
961.65288511
0.4%
898.8328131
0.4%
850.31404091
0.4%
842.84816631
0.4%

2018
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct232
Distinct (%)98.7%
Missing31
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean222.1199085
Minimum0.19050669
Maximum2762.004709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:48:28.382642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.19050669
5-th percentile7.591436575
Q130.33868138
median103.0226492
Q3248.5328662
95-th percentile834.0600977
Maximum2762.004709
Range2761.814202
Interquartile range (IQR)218.1941848

Descriptive statistics

Standard deviation313.4348287
Coefficient of variation (CV)1.411106419
Kurtosis18.5552895
Mean222.1199085
Median Absolute Deviation (MAD)82.33299305
Skewness3.295735133
Sum52198.17851
Variance98241.39184
MonotonicityNot monotonic
2022-04-03T11:48:28.505311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112.74335882
 
0.8%
24.310840752
 
0.8%
32.932835262
 
0.8%
53.355056761
 
0.4%
3.455277061
 
0.4%
75.898946261
 
0.4%
609.80488011
 
0.4%
5.798537311
 
0.4%
32.878576221
 
0.4%
245.78491
 
0.4%
Other values (222)222
83.5%
(Missing)31
 
11.7%
ValueCountFrequency (%)
0.190506691
0.4%
3.455277061
0.4%
3.553226261
0.4%
3.866067831
0.4%
4.798094971
0.4%
5.349136241
0.4%
5.798537311
0.4%
6.109654591
0.4%
6.18662661
0.4%
6.45453071
0.4%
ValueCountFrequency (%)
2762.0047091
0.4%
1188.6084851
0.4%
1155.4656081
0.4%
1144.1647151
0.4%
1042.8141181
0.4%
1041.4004731
0.4%
956.0237051
0.4%
944.33425021
0.4%
936.35554331
0.4%
863.89871541
0.4%

2019
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct231
Distinct (%)98.7%
Missing32
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean218.1616213
Minimum0.18081748
Maximum2444.924659
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:48:28.630952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.18081748
5-th percentile7.392532385
Q128.61892914
median103.0477273
Q3257.0514447
95-th percentile793.7924541
Maximum2444.924659
Range2444.743842
Interquartile range (IQR)228.4325155

Descriptive statistics

Standard deviation297.004557
Coefficient of variation (CV)1.36139691
Kurtosis13.954314
Mean218.1616213
Median Absolute Deviation (MAD)82.76171487
Skewness2.912320056
Sum51049.81937
Variance88211.70687
MonotonicityNot monotonic
2022-04-03T11:48:28.768119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.80459312
 
0.8%
33.943119072
 
0.8%
120.49390282
 
0.8%
52.183275781
 
0.4%
3.849186461
 
0.4%
73.336622551
 
0.4%
591.35060831
 
0.4%
5.326218781
 
0.4%
31.121406971
 
0.4%
257.13274941
 
0.4%
Other values (221)221
83.1%
(Missing)32
 
12.0%
ValueCountFrequency (%)
0.180817481
0.4%
3.144531351
0.4%
3.849186461
0.4%
3.937502441
0.4%
5.075320051
0.4%
5.126972081
0.4%
5.326218781
0.4%
5.999398211
0.4%
6.454467531
0.4%
6.455560621
0.4%
ValueCountFrequency (%)
2444.9246591
0.4%
1235.3388151
0.4%
1185.7801281
0.4%
1109.8085911
0.4%
999.78458741
0.4%
970.25182461
0.4%
901.44668381
0.4%
877.25852941
0.4%
867.36731891
0.4%
850.62631251
0.4%

2020
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

Interactions

2022-04-03T11:48:23.980472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:14.987735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:15.982561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:16.880859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:18.123687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:19.041116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:20.010713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:20.935029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:21.862893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:23.056700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:24.073727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:15.084476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:16.072296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:16.970220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:18.213419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:19.133843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:20.103525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:21.026783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:21.955644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:23.151423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:24.163487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:15.215539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:16.161063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:17.344236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:18.301216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:19.230583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:20.193283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:21.118565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:22.044090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:23.239695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:24.254272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:15.304336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:16.247803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:17.429979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:18.388981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:19.322366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:20.283016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:21.207448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:22.133660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:23.329046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:24.342009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:15.400586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:16.333596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:17.516746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:18.475778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:19.414093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:20.371833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:21.299205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:22.223424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:23.420834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:24.436785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:15.503793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:16.427350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:17.654393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:18.567505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:19.541863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:20.465127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:21.395916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:22.315659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:23.514589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:24.536489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:15.601562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:16.516831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:17.746132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:18.674438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:19.636632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:20.557474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:21.489693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:22.405442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:23.607368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:24.635162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:15.695280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:16.607563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:17.838885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:18.770307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:19.729361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:20.655748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:21.583414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:22.499169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:23.701090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:24.731904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:15.793048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:16.699341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:17.931668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:18.859576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:19.821651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:20.749498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:21.676700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:22.876188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:23.793840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:24.831636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:15.886791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:16.789134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:18.030906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:18.950364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:19.917957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:20.841276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:21.770507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:22.967942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:48:23.886158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-03T11:48:28.893579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-03T11:48:29.070735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-03T11:48:29.248263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-03T11:48:29.420799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-03T11:48:25.036654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-03T11:48:25.277690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-03T11:48:25.459536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-03T11:48:25.627080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Country NameCountry Code199020002011201220132014201520162017201820192020
0AfghanistanAFGNaNNaN37.25279738.31590139.65127443.10952046.17228945.72794949.59660553.35505752.183276NaN
1AlbaniaALBNaN48.694504104.573283107.680971120.567954131.07268083.48671884.372034100.586739122.568022NaNNaN
2AlgeriaDZANaN15.88482580.10446886.29280191.48969295.87503282.24240980.53248983.96160483.53209283.009365NaN
3American SamoaASMNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4AndorraANDNaN202.130674392.640147357.244597361.629888351.727727326.986399329.139413337.432852383.396912359.894321NaN
5AngolaAGONaN3.54932821.97174421.52809530.65902935.38184336.40043833.49248539.01212931.98357826.722197NaN
6Antigua and BarbudaATGNaN113.914614178.815029195.599996190.349229200.935189177.478310214.226623199.269781191.339957184.479048NaN
7ArgentinaARGNaN206.282630324.972287356.986771381.251851347.820349397.150231341.084051395.284762312.769152261.647328NaN
8ArmeniaARMNaN16.202898259.236240264.305246328.379980334.650738297.926873288.278518342.158546355.884587444.321330NaN
9ArubaABWNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Last rows

Country NameCountry Code199020002011201220132014201520162017201820192020
256Post-demographic dividendPSTNaN397.538378730.002788734.430990738.795417751.780061716.574515735.577147755.622023794.742668800.008553NaN
257Pre-demographic dividendPRENaN9.89111732.23541835.05184238.81439839.61602937.77325736.17667836.38814430.55612230.115342NaN
258Small statesSSTNaN68.524492142.133947133.929818141.356668147.784404136.845159140.650173151.860269161.487928151.096989NaN
259South AsiaSASNaN12.06694927.95710928.31947334.91592735.25089535.62906836.01166731.48047532.93283533.943119NaN
260South Asia (IDA & IBRD)TSANaN12.06694927.95710928.31947334.91592735.25089535.62906836.01166731.48047532.93283533.943119NaN
261Sub-Saharan AfricaSSFNaN11.01832530.61951130.23623933.17297532.62470430.43404028.61755929.16951724.31084123.804593NaN
262Sub-Saharan Africa (excluding high income)SSANaN11.01176830.60639830.22295633.16068132.60836030.41845228.60172029.15450624.29455523.787772NaN
263Sub-Saharan Africa (IDA & IBRD countries)TSSNaN11.01832530.61951130.23623933.17297532.62470430.43404028.61755929.16951724.31084123.804593NaN
264Upper middle incomeUMCNaN44.952497130.595665141.828352152.579335155.742938144.818900144.939272162.056941172.575524177.433433NaN
265WorldWLDNaN92.209928183.053537186.818170191.940141193.263322182.623045185.313881191.812265199.859521201.733307NaN